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Lena Verscht

Publications and source records attributed to Lena Verscht.

5 recordsLinked to original sources
arXiv2026

Multiobjective Preexpectation Reasoning for Probabilistic Programs

Probabilistic programs with nondeterminism model planning problems in which a strategy resolves the nondeterminism to optimize an expected outcome. We study the multiobjective setting, optimizing several outcomes at once along a Pareto front, and provide a deductive, program-level account of strategy synthesis. Its core is a multiobjective preexpectation transformer mapping a tuple of postexpectations to the set of simultaneously achievable values, an element of the convex Hoare powerdomain. It conservatively extends weakest preexpectations and lifts standard loop rules. We develop rules to synthesize witnessing strategies as mixed determinizations that randomize over non-probabilistic determinizations. We prove the transformer and synthesis rules sound against an operational MDP semantics, without requiring a finite state space: our approach can be seen as a symbolic approach - at program level - for multiobjective optimization over infinite MDPs. We demonstrate our machinery using various case studies.

cs.PL↗
arXiv2025

Partial Incorrectness Logic

Reasoning about program correctness has been a central topic in static analysis for many years, with Hoare logic (HL) playing an important role. The key notions in HL are partial and total correctness. Both require that program executions starting in a specified set of initial states (the precondition) reach a designated set of final states (the postcondition). Partial correctness is more lenient in that it does not require termination, effectively deeming divergence acceptable. We explore partial incorrectness logic, which stands in relation to O'Hearn's "total" incorrectness logic as partial correctness does to total correctness: Partial correctness allows divergence, partial incorrectness allows unreachability. While the duality between divergence and unreachability may not be immediately apparent, we explore this relationship further. Our chosen formalism is predicate transformers \`a la Dijkstra. We focus here on deterministic and reversible programs, though the discussion extends to nondeterministic and irreversible computations, both of which introduce additional nondeterminism that must be addressed.

cs.LO↗
arXiv2024

A Taxonomy of Hoare-Like Logics: Towards a Holistic View using Predicate Transformers and Kleene Algebras with Top and Tests

We study Hoare-like logics, including partial and total correctness Hoare logic, incorrectness logic, Lisbon logic, and many others through the lens of predicate transformers \`a la Dijkstra and through the lens of Kleene algebra with top and tests (TopKAT). Our main goal is to give an overview - a taxonomy - of how these program logics relate, in particular under different assumptions like for example program termination, determinism, and reversibility. As a byproduct, we obtain a TopKAT characterization of Lisbon logic, which - to the best of our knowledge - is a novel result.

cs.PL↗
arXiv2022

A Calculus for Amortized Expected Runtimes

We develop a weakest-precondition-style calculus \`a la Dijkstra for reasoning about amortized expected runtimes of randomized algorithms with access to dynamic memory - the $\textsf{aert}$ calculus. Our calculus is truly quantitative, i.e. instead of Boolean valued predicates, it manipulates real-valued functions. En route to the $\textsf{aert}$ calculus, we study the $\textsf{ert}$ calculus for reasoning about expected runtimes of Kaminski et al. [2018] extended by capabilities for handling dynamic memory, thus enabling compositional and local reasoning about randomized data structures. This extension employs runtime separation logic, which has been foreshadowed by Matheja [2020] and then implemented in Isabelle/HOL by Haslbeck [2021]. In addition to Haslbeck's results, we further prove soundness of the so-extended $\textsf{ert}$ calculus with respect to an operational Markov decision process model featuring countably-branching nondeterminism, provide intuitive explanations, and provide proof rules enabling separation logic-style verification for upper bounds on expected runtimes. Finally, we build the so-called potential method for amortized analysis into the $\textsf{ert}$ calculus, thus obtaining the $\textsf{aert}$ calculus. Since one needs to be able to handle changes in potential which can be negative, the $\textsf{aert}$ calculus needs to be capable of handling signed random variables. A particularly pleasing feature of our solution is that, unlike e.g. Kozen [1985], we obtain a loop rule for our signed random variables, and furthermore, unlike e.g. Kaminski and Katoen [2017], the $\textsf{aert}$ calculus makes do without the need for involved technical machinery keeping track of the integrability of the random variables. Finally, we present case studies, including a formal analysis of a randomized delete-insert-find-any set data structure [Brodal et al. 1996].

cs.LO↗